A process can include transmitting a pre-trained machine learning model to an edge compute unit associated with a request. The edge compute unit can perform inference using the pre-trained machine learning model and one or more sensor data streams obtained at an edge location. One or more batch uploads of information can be received, associated with inference performed by the edge compute unit and using the pre-trained machine learning model. One or more updated machine learning models can be generated, based on using the batch uploads of information from the edge compute unit to retrain or finetune the pre-trained machine learning model. The one or more updated machine learning models can be transmitted to the edge compute unit, wherein transmission of the updated machine learning model is responsive to receiving the one or more batch uploads of information.
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3. The method of claim 2, wherein the selected ML or AI application utilizes one or more pre-trained machine learning models, including the pre-trained SLAM machine learning model corresponding to the request.
5. The method of claim 4, wherein the connected edge asset requirement of the ML or AI application is indicative of one or more types of input data required for the ML or AI application, and wherein the one or more types of input data required correspond to a set of connected edge asset types.
7. The method of claim 4, wherein the connected edge asset requirement is indicative of one or more sensor types for generating the one or more sensor data streams obtained at the edge location.
8. The method of claim 4, wherein the connected edge asset requirement is indicative of one or more robotic assets associated with obtaining the one or more sensor data streams at the edge location.
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September 5, 2023
October 29, 2024
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